Autism spectrum disorder (ASD) handicaps the social and communicative abilities of 1 out of every 110 children in the United States. Evidence suggests that individual variation in social behavior in ASD, as well as within the typically developing human population, arises from a combination of genetic predispositions and individual experience, yet the underlying biological mechanisms remain poorly understood. Progress lags due to the lack of a suitable animal model in which natural variation in both underlying genetics and individual experience generates heterogeneity in social behavior that is qualitatively similar, if not homologous, to that in humans. Development of an animal model with natural social variation homologous to that of humans will permit us to more effectively target interventions that directly impact the neural circuits mediating behaviors impaired in ASD. To address this gap, we will develop a fully-realized biological model of the genetic contributions to social behavior phenotype by characterizing social behavior and cognition and their genetic foundations in a large population of animals living in naturalistic circumstances with minimal external intervention. We will use observational techniques and field experiments to quantitatively define heterogeneity in social temperament and social cognition phenotypes in males and females. In our stratified approach, we will assess social temperament using intensive observation of natural behavior and social cognition using analogs of standard laboratory tasks. We will also assay genetic variation using a stratified approach. We will first use an a priori approach that assays gene polymorphisms previously implicated in social behavior, as well as polymorphisms in the same or related pathways that have yet to be assessed in macaques. Genetic variation will include repeat length polymorphisms (VNTRs) as well as single nucleotide polymorphisms (SNPs) and will be used to identify genetic biomarkers for social phenotypes. In parallel, we will use a data-driven, bottom-up approach to identify new genomic variants linked to social behavior and cognition, by conducting full genome sequencing on 50 key individuals identified by pedigree and social temperament. Variants identified by whole-genome sequencing of selected individuals then will be assayed across the entire population to assess statistical correlations with social behaviors. Computational techniques will be used to develop biologically-meaningful measures of the relationships between phenotypic and genotypic variation. Bioinformatics software and infrastructure, previously developed and extensively validated by our group for analysis of genomic variants in human populations, will be adapted to identify and annotate genomic variations in the macaque population, and to identify variants correlated with specific measures of social behavior. These data will be combined with life history and pedigree data to generate predictive models of the impact of genotype and social phenotype on biological success.